Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "140" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 14 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 14 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459989 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.224800 | -0.879046 | -0.729076 | -0.077537 | -0.651599 | -0.857270 | 2.655217 | 2.182916 | 0.6261 | 0.6428 | 0.3778 | nan | nan |
| 2459988 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.420314 | -0.802509 | -0.907803 | -0.046536 | -0.600258 | -0.645660 | 4.603409 | 2.513389 | 0.6280 | 0.6445 | 0.3700 | nan | nan |
| 2459987 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.034501 | -0.937608 | -0.766214 | -0.348363 | -0.641192 | -0.957226 | 3.084662 | 2.510098 | 0.6371 | 0.6520 | 0.3669 | nan | nan |
| 2459986 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.492414 | -0.901082 | -0.794782 | -0.192032 | -0.683045 | -0.553591 | 1.963631 | 0.707923 | 0.6553 | 0.6717 | 0.3268 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 12.085532 | -1.366284 | -1.078421 | -0.356615 | 7.246334 | -0.465279 | 158.457144 | 20.527561 | 0.5953 | 0.6440 | 0.3693 | nan | nan |
| 2459984 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.384358 | -0.899974 | -0.317842 | -0.407446 | 16.423680 | -1.428476 | 3.212782 | 1.061794 | 0.6447 | 0.6631 | 0.3522 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.263152 | -1.004207 | -0.794211 | -0.141711 | -0.478698 | -0.793563 | 2.746935 | 1.127600 | 0.6623 | 0.6842 | 0.3140 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.063364 | -1.284147 | -0.514884 | -0.517636 | -0.370438 | -1.093809 | -0.078904 | -1.236296 | 0.7120 | 0.7119 | 0.2751 | nan | nan |
| 2459981 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.480580 | -1.129001 | -1.328646 | 0.064546 | 9.359503 | -0.413132 | 34.916501 | 4.724154 | 0.6240 | 0.6513 | 0.3706 | nan | nan |
| 2459980 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.185838 | -1.030410 | -0.991759 | -0.456738 | -0.455858 | -1.398423 | -0.224202 | -0.782407 | 0.6777 | 0.6893 | 0.2983 | nan | nan |
| 2459979 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.261105 | -0.967996 | -1.088020 | -0.370139 | -0.743257 | -1.255924 | 3.016182 | 2.548787 | 0.6284 | 0.6507 | 0.3748 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.293721 | -0.836121 | -1.099808 | -0.223203 | -0.664724 | -0.662223 | 4.947114 | 3.295500 | 0.6293 | 0.6496 | 0.3811 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.524811 | -0.832179 | -0.980169 | -0.417902 | -0.454649 | -1.046771 | 2.942177 | 2.209368 | 0.5957 | 0.6161 | 0.3442 | nan | nan |
| 2459976 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.424894 | -0.833188 | -1.037911 | -0.313012 | -0.614660 | -0.249495 | 3.136758 | 2.676482 | 0.6330 | 0.6529 | 0.3737 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 2.655217 | -0.879046 | 0.224800 | -0.077537 | -0.729076 | -0.857270 | -0.651599 | 2.182916 | 2.655217 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 4.603409 | -0.802509 | 0.420314 | -0.046536 | -0.907803 | -0.645660 | -0.600258 | 2.513389 | 4.603409 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 3.084662 | 0.034501 | -0.937608 | -0.766214 | -0.348363 | -0.641192 | -0.957226 | 3.084662 | 2.510098 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 1.963631 | -0.901082 | 0.492414 | -0.192032 | -0.794782 | -0.553591 | -0.683045 | 0.707923 | 1.963631 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 158.457144 | -1.366284 | 12.085532 | -0.356615 | -1.078421 | -0.465279 | 7.246334 | 20.527561 | 158.457144 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Variability | 16.423680 | 0.384358 | -0.899974 | -0.317842 | -0.407446 | 16.423680 | -1.428476 | 3.212782 | 1.061794 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 2.746935 | 0.263152 | -1.004207 | -0.794211 | -0.141711 | -0.478698 | -0.793563 | 2.746935 | 1.127600 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Shape | 0.063364 | 0.063364 | -1.284147 | -0.514884 | -0.517636 | -0.370438 | -1.093809 | -0.078904 | -1.236296 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 34.916501 | -1.129001 | 2.480580 | 0.064546 | -1.328646 | -0.413132 | 9.359503 | 4.724154 | 34.916501 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Shape | 0.185838 | -1.030410 | 0.185838 | -0.456738 | -0.991759 | -1.398423 | -0.455858 | -0.782407 | -0.224202 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 3.016182 | 0.261105 | -0.967996 | -1.088020 | -0.370139 | -0.743257 | -1.255924 | 3.016182 | 2.548787 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 4.947114 | -0.836121 | 0.293721 | -0.223203 | -1.099808 | -0.662223 | -0.664724 | 3.295500 | 4.947114 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 2.942177 | 0.524811 | -0.832179 | -0.980169 | -0.417902 | -0.454649 | -1.046771 | 2.942177 | 2.209368 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | N13 | digital_ok | ee Temporal Discontinuties | 3.136758 | -0.833188 | 0.424894 | -0.313012 | -1.037911 | -0.249495 | -0.614660 | 2.676482 | 3.136758 |